Viral load of episomal and integrated forms of human papillomavirus type 33 in high‐grade squamous intraepithelial lesions of the uterine cervix
Bibliographic record
Abstract
The association between total and integrated HPV-33 DNA loads and high-grade squamous intraepithelial lesions (HSIL) of the uterine cervix was investigated. Of 5,347 women recruited in 4 studies, 89 (64 without SIL, 7 low-grade SIL (LSIL), 15 HSIL, 3 unknown grade) were infected by HPV-33. HPV-33 E6, HPV-33 E2 and beta-globin DNA were measured with real-time PCR that allowed to assess total (E6), episomal (E2) and integrated (E6-E2) HPV-33 viral loads. HPV-33 E6/E2 ratios >/=>/=2.0 suggesting the presence of integrated HPV-33 were obtained for 28.6% (n = 18) of women without SIL and 21.4% (n = 3) of women with HSIL (p = 0.74). Although median viral loads were similar, there was a trend toward having a greater proportion of women with HSIL in the fourth quartile (>/=>/=10(6.69) copies/mug DNA) of total HPV-33 viral loads compared to normal women. Controlling for age, site, ethnicity and LCR polymorphism by logistic regression, HPV-33 total loads in the fourth quartile {odds ratio (OR) 4.5 [95% confidence interval (CI) 1.2-17.3]; p = 0.03} and episomal loads in the fourth quartile (>/=>/=10(6.64) copies/mug DNA) [OR 3.9 (95% CI 1.1-13.2); p = 0.05] but not integrated HPV-33 load in the fourth quartile [OR 1.0 (95% CI 0.3-3.3); p = 0.50] were associated with HSIL. Controlling for age, study site and SIL grade, HPV-33 episomal load [OR 0.2 (95% CI 0.1-0.5), p = 0.0004] was associated with the presence of HPV-33 integration. High episomal loads in HSIL and the presence of integration in women without SIL are likely to weaken the usefulness of HPV load of integrated forms in clinical practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".